Ontologies are structures, used for knowledge representation, which model domain knowledge in the form of concepts, roles, instances and their relationships. This knowledge can be exploited by an assessment system in the form of multiple choice questions (MCQs). The existing approaches which use ontologies expressed in the Web Ontology Language (OWL) for MCQ generation, are limited to simple concept related questions — “What is C?” or “Which of the following is an example of C?” (Where C is a concept symbol) — Or analogy type questions involving roles. There are no efforts in the literature which make use of the terminological axioms in the ontology such as existential, universal and cardinality restrictions on concepts and roles for MCQ generation. A probabilistic framework is introduced for reducing the in here uncertainty of trajectory data collected for RFID-monitored objects. The framework represents the position of an object at each instant as a random variable over the set of possible locations. The probability density function of this random variable is initialized according to an apriority probability distribution, and then revised by conditioning the event that integrity constraints are satisfied.